Agentic AI and the New Frontier of Autonomous Digital Workflows: A Critical Sociology of Power, Institutions, and Global Inequality (2025)
- OUS Academy in Switzerland
- Oct 8
- 13 min read
Author: Zarina Akhmetova
Affiliation: Independent Researcher
Abstract
Agentic artificial intelligence—AI that can plan, act, and adapt across multi-step tasks—has moved from experimental demos to enterprise pilots in 2025. This paper offers a critical sociological account of agentic AI as a socio-technical regime that reorganizes work, redistributes power, and reconfigures global value chains. Drawing on Bourdieu’s concept of capital and field, world-systems analysis, and institutional isomorphism, the article theorizes how agentic AI systems alter organizational decision-making, create new forms of symbolic authority, and intensify core–periphery dependencies in compute, data, and standards. Rather than treating “autonomy” as a purely technical attribute, the paper situates autonomy within governance, labor relations, and institutional pressures. The analysis proposes a “bounded autonomy” framework—rights, limits, and accountabilities—for managers and policymakers; outlines implications for technology management, tourism operations, and service supply chains; and identifies open research questions on safety, explainability, and cross-border governance. The contribution is a theoretically grounded map for understanding how agentic AI reshapes power relations while promising productivity and innovation.
Keywords: agentic AI; autonomous workflows; AI governance; Bourdieu; world-systems; institutional isomorphism; human-in-the-loop
1. Introduction: From Assistance to Agency
In the last decade, AI was largely framed as assistive: a predictive tool that recommends, classifies, or summarizes. In 2025, organizations increasingly experiment with agentic AI—systems that decide and do. These systems translate goals into multi-step plans, call tools and APIs, monitor outcomes, and revise strategies when conditions shift. The change seems technical; yet, its deeper meaning is sociological. When an AI acts, it reassigns discretion, reallocates attention, and recasts blame. A helpmate becomes a partial coworker. Autonomy enters the division of labor.
This article argues that agentic AI must be examined not only through engineering metrics (latency, accuracy, cost) but through theories of power, institutional order, and global stratification. Three perspectives guide the analysis:
Bourdieu’s capital and field: How does agentic AI reweight economic, cultural, social, and symbolic capital across organizational fields?
World-systems theory: How do compute concentration, data access, and standards position certain economies as “core” and others as “periphery”?
Institutional isomorphism: Why do firms converge on similar agentic AI practices—even when uncertainties remain—through coercive, mimetic, and normative pressures?
The article also recognizes labor process concerns (deskilling/reskilling, surveillance), governance dilemmas (accountability, auditability), and practical management choices (scoping, risk limits, human oversight). Across sectors—technology, services, tourism, logistics—the promise of agentic AI is real. So are the politics embedded in how “autonomy” is designed, delegated, and defended.
2. Theoretical Background
2.1 Bourdieu: Capital, Field, and the Struggle for Distinction
Bourdieu’s framework highlights how actors compete within fields using different capitals:
Economic capital (funding, compute capacity),
Cultural capital (expertise, engineering know-how),
Social capital (partnerships, data-sharing consortia),
Symbolic capital (prestige, legitimacy, certifications).
Agentic AI reorganizes these capitals. Firms with abundant compute and engineering talent convert economic and cultural capital into symbolic capital by showcasing autonomous workflows. Certifications, benchmarks, and “responsible AI” labels crystallize symbolic capital—conferring legitimacy that influences procurement and regulation. Within firms, teams controlling the “agent platform” gain cultural and symbolic capital relative to business units dependent on them, reshaping intra-organizational hierarchies.
2.2 World-Systems: Core, Periphery, and Semiperiphery in the Age of Compute
World-systems theory interprets the global economy as a network dominated by core regions capturing high-value activities while periphery regions supply lower-value inputs. In the AI era, core status is linked to:
Sovereign access to advanced semiconductors and cloud,
Proprietary frontier models and data pipelines,
Standard-setting clout (benchmarks, safety protocols, API conventions).
Peripheral and semiperipheral regions often depend on imported models, rented compute, and external compliance templates. Agentic AI can widen gaps: value concentrates where model innovation and orchestration platforms reside. Yet, semiperipheries can climb by specializing in domain-specific agents (e.g., tourism operations, smart mobility) and building regional data commons and public compute.
2.3 Institutional Isomorphism: Why Organizations Converge
DiMaggio and Powell’s isomorphism explains why organizations become similar when facing uncertainty:
Coercive: compliance with regulation, procurement rules, audits.
Mimetic: imitation under uncertainty (“agent copilot” bandwagoning).
Normative: professional standards from associations, consultancies, and journals.
Agentic AI adoption exhibits all three. Regulated sectors may face coercive requirements for logs, human override, and incident reporting; managers mimetically copy “agent frameworks” from perceived leaders; and normative pressures arise from codes of practice, certification schemas, and professional training.
3. What Is Agentic AI? A Socio-Technical Definition
Technically, agentic AI integrates: (a) reasoning/planning, (b) tool-use via APIs, (c) memory for context, (d) monitoring/feedback loops, and (e) policy constraints. Sociologically, it is a delegation apparatus: a system that transforms intentions (“optimize this campaign,” “reconcile these invoices,” “triage these requests”) into sequences of authorized actions across information systems.
Autonomy is never total. It is bounded by permissions, scopes, and escalation rules. The decisive managerial act is not building autonomy but governing it: deciding when an agent may act, which tools it can call, which thresholds trigger human review, and how performance is explained and contested.
4. Method and Scope
This paper offers a conceptual and integrative review rather than an empirical test. It synthesizes scholarship in sociology of organizations, political economy, and information systems to interpret the present shift toward agentic AI. Illustrative scenarios are drawn from service operations, tourism management, and technology workflows to ground the argument. The aim is to provide managers, policymakers, and researchers a shared theoretical language to interrogate claims of efficiency and innovation.
5. Analysis
5.1 Capital Reallocation Inside the Firm
Economic capital concentrates in teams that own orchestration platforms and model-ops. Budgetary power follows their roadmaps; business units must queue for features, exposing symbolic dependence. Cultural capital accrues to engineers who understand safety constraints, tool schemas, and evaluation harnesses; their expertise becomes scarce and valorized. Social capital emerges where cross-functional coalitions form—legal, compliance, IT security—able to negotiate risk limits and win executive sponsorship. Symbolic capital coalesces around “responsible autonomy” narratives: dashboards, safety gates, and audit trails that perform legitimacy to boards and regulators.
Implication: Strategy is subtly re-centered toward the platform teams. The politics of backlogs and permissions becomes a politics of autonomy—who may act, in which systems, and under what justifications.
5.2 From Assistive Predictions to Agentic Decisions
Assistive AI was a voice in the room; agentic AI is a hand on the keyboard. This shift alters accountability. When an agent books inventory, adjusts prices, or sends escalations, it leaves performative traces (logs, prompts, tool calls). These traces are ambiguous: they promise transparency yet also create an illusion of control—overly persuasive dashboards can mask specification gaming or hidden biases.
Bounded autonomy requires: (1) explicit task contracts (goals, constraints, escalation points), (2) dual-control mechanisms (randomized human checks, four-eyes rules), and (3) post-hoc sensemaking (root-cause reviews with sociotechnical inputs, not just metrics).
5.3 World-Systems Dynamics of Compute and Data
Compute and data are the new merchant fleets. Cores control fabrication know-how, hyperscale clouds, and frontier models; peripheries rent access. Standards originate in the core, then diffuse outward through SDKs, compliance kits, and benchmarks.
Upgrading strategies for semiperipheries:
Invest in public or consortium compute accessible to universities and SMEs,
Nurture domain-specific agents (e.g., sustainable tourism itineraries rooted in local cultural assets),
Develop regional data trusts with clear consent and value-sharing,
Participate early in standards fora to avoid one-way dependency.
Without such moves, agentic AI may intensify “value siphoning,” where margins accrue to platform owners while downstream implementers absorb integration risks.
5.4 Institutional Isomorphism in Practice
Coercive: Auditors require evidence of human-in-the-loop for safety-critical actions; procurement mandates certification of logging and rollback features.
Mimetic: Firms adopt “agents for everything” playbooks and replicate sample apps—even when their data maturity is low.
Normative: Professional bodies teach risk taxonomies, evaluation protocols, and prompt hygiene that standardize practice across firms.
Isomorphism can be productive—reducing avoidable harms—but also stifling if it locks in premature standards. A critical task for leaders is to separate safety convergence (good) from innovation lock-in (bad).
5.5 Labor Process, Skills, and Habitus
Agentic AI reorganizes skill. Routine multi-step digital work (reconciliation, scheduling, routing) is automatable. Yet the habitus of high-reliability operations—tacit skills of noticing weak signals, interpreting social context, and negotiating exceptions—remains human-centered. Rather than crude deskilling, we see bi-modal reskilling:
A platform stratum (prompt engineers, toolsmiths, safety evaluators),
A frontline stratum (exception handlers, client communicators, domain interpreters).
Where training investments lag, the gap becomes a new inequality: those who can shape autonomy vs those who merely supervise it.
5.6 Surveillance, Control, and Symbolic Violence
Dashboards that portray “agent reliability” can legitimate tighter controls over human workers—work tempos, escalation thresholds, and “acceptable deviation” bands. This may enact symbolic violence (in Bourdieu’s sense) by naturalizing managerial choices as technical necessities. Transparency must not become a one-way mirror. Worker councils and ethics committees should have access to the same logs and the power to contest parameters.
5.7 Tourism and Service Supply Chains: A Focused Lens
Tourism is an algorithmically rich domain: dynamic pricing, itinerary planning, demand sensing, sustainability routing, and multilingual support.
Agentic itinerary planners can optimize flows for carbon and congestion, but must encode cultural capital: respect for heritage, local customs, and seasonal rhythms.
Destination management organizations can deploy agents for capacity management, yet risk marginalizing local operators if standards and APIs privilege large platforms.
Symbolic capital matters: destinations that brand themselves as “responsible AI ready” can attract investment and shape norms.
A world-systems lens warns that uncritical adoption may lock destinations into dependency on external platforms; a Bourdieu lens urges elevation of local knowledge and community ties as legitimate capital in the optimization loop.
5.8 Safety, Explainability, and the Pragmatics of Trust
Trust in autonomy is earned in everyday reliability, not slogans. Practical measures:
Task scoping with negative permissions (what the agent may not do),
Checkpoints for high-impact actions (payments, price changes, legal notices),
Counterfactual logs that show plausible alternatives the agent considered,
Human challenge rights for staff to pause or roll back,
Diverse evaluation sets reflecting edge cases and minority impacts.
Explainability must be operational—not abstract rationales but actionable traces that support learning, remediation, and fair accountability.
5.9 Governance: The Triangle of Rights, Limits, and Accountabilities
The proposed bounded autonomy model articulates:
Rights – what an agent may do (tools, data scopes, schedules).
Limits – guardrails (spend caps, rate limits, sensitive-data blocks, jurisdictional constraints).
Accountabilities – who signs off, who monitors, who answers for incidents, and how restitution works.
This triangle should be codified as task contracts attached to each agent, maintained in version control, and visible to stakeholders. Governance is most credible when it fuses legal compliance with participatory oversight (workers, customers, community representatives).
5.10 Measuring Value Without Hiding Costs
Agentic AI’s ROI depends on complete accounting. Benefits (throughput, lead time, recovery speed) must be weighed against costs: integration debt, safety engineering, monitoring staff, incident response. Shadow costs—reputation risk, talent churn from perceived deskilling, supplier lock-in—belong on the ledger. A transparent scorecard maintains legitimacy and prevents over-financialization of safety.
5.11 Standards, Semantics, and the Politics of Interoperability
Schemas for tools, events, and traces are not neutral. The actors who define them shape what counts as valid action, acceptable evidence, and sufficient explanation. Participation by public institutions, universities, and SMEs in standards bodies can counterbalance narrow interests. Interoperability is a public good; without it, peripheries pay recurring “translation tolls.”
5.12 Multi-Agent Systems and Emergent Coordination
As organizations deploy multiple agents—pricing, procurement, support—coordination becomes a second-order problem. Conflicting objectives (cost vs service level) require arbitration. Sociologically, this resembles bureaucratic politics: agents are proxies for departmental priorities. Explicit meta-policies (priority rules, tie-breakers, escalation) are crucial to prevent emergent failure modes and to keep human strategy in command.
6. Managerial Implications and Roadmap
6.1 Strategic Positioning
Pick domains with clear feedback (billing, routing, content QA) before brand-critical actions.
Invest in cultural capital: training for safety, evaluation, and domain reasoning.
Build social capital: coalitions across legal, risk, and operations to co-own autonomy.
6.2 Operating Model
Human-in-the-loop by design: structured interventions at uncertain points.
Task contracts: machine-readable rights/limits/accountabilities per agent.
Red-team and rehearsal: simulate failure and recovery with business owners present.
Dual metrics: combine throughput with fairness, explainability, and worker well-being.
6.3 Technology Stack
Orchestration platform with role-based access and immutable logs.
Evaluation harness with diverse scenarios and minority stress tests.
Data governance: consent, lineage, and jurisdictional controls.
Interoperability: avoid single-vendor lock-in by supporting open schemas and portable traces.
6.4 Workforce and Culture
Reskilling pathways into agent design, testing, and exception handling.
Right to challenge: empower staff to pause agents without career penalties.
Participatory reviews: include frontline workers in post-incident learning.
Ethical literacy: train on bias, specification gaming, and explainability.
6.5 Sector Notes
Technology & Services: prioritize back-office automations with measurable KPIs.
Tourism & Hospitality: encode sustainability and cultural heritage as first-class objectives, not peripheral constraints; ensure small operators can plug into the platform on equitable terms.
Public Services: transparency, appeal rights, and due process are non-negotiable; pilot in advisory tasks before adjudication contexts.
7. Findings
Agentic AI changes who holds discretion, reallocating capital to platform teams and those who can ritualize “responsible autonomy.”
Global value capture tilts toward compute- and standards-rich cores, but semiperipheries can upgrade via domain specialization, public compute, and data trusts.
Isomorphic pressures push firms toward similar adoption patterns; leaders must distinguish prudent safety convergence from costly lock-in.
Labor impacts are not simple displacement; they create a stratified skill ecology where habitus for exception handling and sensemaking becomes vital.
Governance is the decisive innovation: bounded autonomy contracts, dual-control mechanisms, and operational explainability generate durable trust.
Measuring ROI without shadow costs leads to brittle deployments; legitimacy demands full sociotechnical accounting.
Interoperability is political: without plural participation, standards risk encoding narrow interests and reproducing dependency.
8. Limitations and Future Research
This article is conceptual. Empirical validation—multi-site case studies, comparative sector analyses, cross-national regulatory outcomes—is needed. Promising lines of inquiry include (a) longitudinal studies of capital shifts within firms, (b) measurement of periphery upgrading via domain agents, (c) ethnographies of exception handling, and (d) audits of interoperability costs across vendor ecosystems. Methodologically, mixed designs that combine log analysis with interviews and organizational documents can illuminate how “autonomy” is enacted day to day.
9. Conclusion
Agentic AI represents a step change in digital operations: from predictions that advise to systems that act. The allure is productivity and responsiveness; the stakes are power, legitimacy, and global equity. A Bourdieusian lens reveals how capitals are reshuffled and new hierarchies formed. A world-systems lens shows how compute, models, and standards can consolidate advantage in the core while offering upgrade paths to the semiperiphery. An institutional lens explains why convergence occurs—and where critical divergence might preserve innovation.
The practical message is clear: autonomy must be bounded. Rights, limits, and accountabilities should be explicit and reviewable. Human-in-the-loop is not an afterthought but an organizing principle. Interoperability and participatory governance convert private efficiency into public legitimacy. When designed with these commitments, agentic AI can expand organizational capabilities while respecting workers, communities, and global fairness. When designed without them, it risks becoming another chapter in unequal development—automation at the core and dependency at the edges.
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